{"id":"W2050673532","doi":"10.1115/2001-gt-0280","title":"Prediction of Bird Impact Pressures and Damage Using MSC/DYTRAN","year":2001,"lang":"en","type":"article","venue":"Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite element method; Robustness (evolution); Computer science; Structural engineering; Applied mathematics; Engineering; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002547532,0.0004944298,0.0003977743,0.0005911813,0.000226877,0.0003998676,0.0006969343,0.0005892998,0.0026554],"category_scores_gemma":[0.0005846682,0.0003029278,0.0003323643,0.0004326872,0.0002614315,0.0003453325,0.0003641394,0.000413886,0.0008214904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000414869,"about_ca_system_score_gemma":0.0005114136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006488386,"about_ca_topic_score_gemma":0.007475676,"domain_scores_codex":[0.999877,0.00001205598,0.000007375825,0.00001470351,0.00007462965,0.00001428919],"domain_scores_gemma":[0.9996388,0.0001341387,0.00004771044,0.00005301219,0.0001108111,0.00001554476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005940438,0.00005599819,0.005711677,0.00008005635,0.00001744859,0.0001367411,0.00006844135,0.9544374,0.01437937,0.002101563,0.001073661,0.02187816],"study_design_scores_gemma":[0.000003279767,0.00002064156,0.001196516,0.000002949968,0.000002851746,0.00003049393,0.000008621478,0.9926864,0.005103637,0.0001375101,0.0008022586,0.000004865531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6348845,0.0002004681,0.3350285,0.0001588085,0.00005700779,0.0001453065,0.002976672,0.004162006,0.02238676],"genre_scores_gemma":[0.9224432,0.0002824067,0.07162073,0.00002511983,0.00000921203,0.0001588267,0.001453472,0.000165383,0.003841543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006488386,"threshold_uncertainty_score":0.01290125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475639767675117,"score_gpt":0.2670006171658252,"score_spread":0.2522442194890741,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}